Éditeur(s) : HAL CCSD
Résumé : International audience
One of the most common tasks in the area of social network mining is the extraction of frequent patterns from social networks. Although traditional approaches have been mainly focused on subgraphs occurring frequently in a network or a set of networks, new approaches have attempted to exploit network structure and node properties in order to elicit new kinds of patterns. One of these news approaches is the extraction of conceptual links, a solution that combines both structure and node properties for providing knowledge on the groups of nodes the most connected in a social network. However, if the extraction of conceptual links offers a great potential in terms of knowledge discovery as well as network visualization, the search for these kinds of patterns remains a computationally intensive problem. Thus, the efficiency of the extraction processes highly depends on the efficiency of the underlying algorithm. In this paper, we focus on the extraction of maximal frequent conceptual links (MFCL) and we propose a hybrid algorithm that applies a filter on the nodes for reducing the search space to the most frequent groups of nodes. Although our solution potentially causes loss of some patterns, we demonstrate its efficiency by comparison with the MFCLMin algorithm. We investigate the potential loss of patterns, the gain on the runtime, and the gain on the number of comparisons.
IEEE International Conference on Research Challenges in Information Science
Paris, France

hal-00841699
https://hal.archives-ouvertes.fr/hal-00841699

Éditeur(s) : HAL CCSD Springer Verlag
Résumé : International audience
The actin cytoskeleton is a dynamic structure that provides an interactive platform for organelles and cellular components. It also serves as track for membranes and vesicles that move via myosin. The actin cytoskeleton of Symbiodinium is a well-organized reticular structure suggestive of multiple membrane interactions, very likely including those of the chloroplast. The Symbiodinium chloroplast membrane network is, in turn, a highly organized structure, suggestive of being under the control of an organizing network. We visualized the chloroplast membranes of cultured Symbiodinium sp. under various light conditions and observed changes dependent on illumination intensity. Since we suspected interaction between these two organelles, and we knew that the Symbiodinium actin cytoskeleton collapses upon treatment with either latrunculin B, an actin microfilament-disrupting agent, or butanedione monoxime, a myosin function inhibitor, we tested the Symbiodinium sp. oxygen evolution in their presence. Upon latrunculin B addition, the oxygen production decreased compared to non-treated cells; however, this was not observed after a 24 h latrunculin treatment. On the contrary, butanedione monoxime treatment caused a non-recoverable dysfunction of the chloroplast causing a severe loss in oxygen production even after long-term exposure. Using electron microscopy, we observed an alteration of the Symbiodinium sp. chloroplast distribution after latrunculin B treatment, with respect to untreated cells. Furthermore, a thorough disorganization of the chloroplast grana was observed after butanedione monoxime treatment. These data suggest that an actomyosin system would be important for chloroplast organization and distribution, and critical for normal photosynthetic function of Symbiodinium sp.
ISSN: 0166-8595

hal-01546164
https://hal.archives-ouvertes.fr/hal-01546164
DOI : 10.1007/s11120-015-0142-9

Éditeur(s) : HAL CCSD
Résumé : The recent accumulation of genomic sequence data has shown that gene evolution is not strictly tree-Like. Many evolutionary processes, like exon shuffling, gene fusion or nonhomologous recombination remodel genes by creating composite structures that are made from parts with different evolutionary histories. The development of sequence similarity networks provides an analytical framework to study the impact of these processes on molecular evolution, by structuring the resemblance relationships between sequences and by formalizing, in terms of graph theory, the detection of composite genes (intransitive triplets) and gene families (clique minimal separators). The size of current data sets, typically several million sequences, has also required the development of new tools and methods: sequence comparison parallelization, large networks visualization with Louvain communities and large cycles identification. When applied to eukaryotic and viral genome data sets, these methods have shown that composite genes are found throughout cellular organisms and mobile genetic elements. Proportionally, composite genes are more numerous in eukaryotic genomes; in absolute number, they are more numerous in viruses. In the latter, composite genes functional distribution is biased (enrichment of genes families that are essential for the perpetuation of the viral cycle), and the various parts of composite genes sometimes even originate from the genetic material of different viral classes. More generally, the extent of combinatorial processes, by unravelling other evolutionary bonds than homology bonds in the strictest sense, legitimates a pluralistic study of similarity relationships between sequences.
L’accumulation récente de données de séquences génomiques a montré que l’évolution des gènes n’est pas strictement arborescente. De nombreux processus évolutifs, comme l’exon shuffling, la fusion de gènes ou la recombinaison illégitime remodèlent les gènes, créant des structures composites, formées de parties dont les histoires évolutives sont différentes. Le développement de réseaux de similarité de séquences fournit un cadre analytique permettant d’étudier l’impact de ces processus sur l’évolution moléculaire, en structurant les relations de ressemblance entre séquences et en formalisant en termes de graphes la détection de gènes (triplets intransitifs) et de familles de gènes (cliques minimales séparatrices) composites. La taille des jeux de données actuels, de l’ordre de plusieurs millions de séquences, a également requis le développement de nouveaux outils et méthodes : parallélisation des comparaisons de séquences, visualisation de très grands réseaux par simplification en communautés de Louvain et identification de grands cycles. Appliquées à des jeux de données de génomes eucaryotes et viraux, ces méthodes ont démontré la présence de gènes composites dans tout le vivant et les éléments génétiques mobiles. En proportion, les gènes composites sont plus nombreux dans les génomes eucaryotes ; en nombre absolu, ils sont plus nombreux à être portés par des virus. Chez ces derniers, la distribution fonctionnelle des gènes composites est biaisée (enrichissement dans les familles essentielles pour la perpétuation du cycle viral), et les éléments des gènes composites trouvent même parfois leurs origines dans le matériel génétique de classes virales différentes. Plus généralement, l’étendue des processus combinatoires, en révélant des liens évolutionnaires autres que les liens d’homologie au sens fort, justifie une étude pluraliste des relations de similarité entre séquences.
https://tel.archives-ouvertes.fr/tel-01127379

NNT : 2014PA066358
tel-01127379
https://tel.archives-ouvertes.fr/tel-01127379
https://tel.archives-ouvertes.fr/tel-01127379/document
https://tel.archives-ouvertes.fr/tel-01127379/file/2014PA066358.pdf

Éditeur(s) : HAL CCSD IGI Global
Résumé : International audience
The social behavior of individuals is an important factor of the transmission and the evolution of many diseases. As such, epidemic studies have attempted to integrate social aspects in dissemination modeling. Since the pioneering works of Klovdahl on AIDS in 1985, epidemiological investigations and interventions increasingly focus on social networks. Significant factors of the transmission and outbreak of many infectious diseases are the structure and nature of human interactions. Network-based modeling approaches have found various applications in epidemiology as a simple yet efficient way to represent the complexity of human relationships implicated in dissemination processes. However, most results have been obtained by considering social networks as steady stage structures. Evolving networks have not been explored. The objective is first to give an overview of network-based modeling attempts in epidemiology to analyze and understand the dissemination processes with an emphasis on dynamic networks. The authors approach is designed to understand the impact of social links dynamics on epidemic spread. The authors present the results obtained by combining network evolution patterns (link creation and deletion) and a typical epidemic model. The speed of link dynamics and the infection time strongly influence the occurrence and value of the epidemic peak.
ISSN: 1947-8186

hal-00700105
https://hal.archives-ouvertes.fr/hal-00700105
DOI : 10.4018/jismd.2012070103

Éditeur(s) : HAL CCSD
Résumé : International audience
The study of complex biological processes requires to forgo simplified models for extensive ones. Yet, these models' size and complexity place them beyond understanding. Their analysis requires new methods for identifying general patterns. The Transforming Growth Factor TGF-β is a multifunctional cytokine that regulates mammalian cell development , differentiation, and homeostasis. Depending on the context, it can play the antagonistic roles of growth inhibitor or of tumor promoter. Its context-dependent pleiotropic nature is associated with complex sig-naling pathways. The most comprehensive model of TGF-β-dependent signaling is composed of 15,934 chains of reactions (trajectories) linking TGF-β to at least one of its 159 target genes. Identifying functional patterns in such a network requires new automated methods. This article presents a framework for identifying groups of similar trajec-tories composed of the same molecules using an exhaustive and without prior assumptions approach. First, the trajectories were clustered using the Relevant Set Correlation model, a shared nearest-neighbors clustering method. Five groups of trajectories were identified. Second, for each cluster the over-represented molecules were determined by scoring the frequency of each molecule implicated in trajectories. Third, Gene set enrichment analysis on the clusters of trajectories revealed some specific TGF-β-dependent biological processes, with different clusters associated to the antagonists roles of TGF-β. This confirms that our approach yields biologically-relevant results. We developed a web interface that facilitates graph visualization and analysis. Our clustering-based method is suitable for identifying families of functionally-similar trajectories in the TGF-β signaling network. It can be generalized to explore any large-scale biological pathways.
CMSB 2017 - 15th International Conference on Computational Methods in Systems Biology
Darmstadt, France

hal-01559249
https://hal.archives-ouvertes.fr/hal-01559249
https://hal.archives-ouvertes.fr/hal-01559249/document
https://hal.archives-ouvertes.fr/hal-01559249/file/JeanCoquet_CMSB.pdf